| harris.test {fastmatrix} | R Documentation |
Test for variance homogeneity of correlated variables
Description
Performs large-sample methods for testing equality of p \ge 2 correlated variables.
Usage
harris.test(x, test = "Wald")
Arguments
x |
a matrix or data frame. As usual, rows are observations and columns are variables. |
test |
test statistic to be used. One of "Wald" (default), "log", "robust" or "log-robust". |
Value
A list of class 'harris.test' with the following elements:
statistic |
value of the statistic, i.e. the value of either Wald test, using the log-transformation, or distribution-robust versions of the test (robust and log-robust). |
parameter |
the degrees of freedom for the test statistic, which is chi-square distributed. |
p.value |
the p-value for the test. |
estimate |
the estimated covariance matrix. |
method |
a character string indicating what type of test was performed. |
References
Harris, P. (1985). Testing the variance homogeneity of correlated variables. Biometrika 72, 103-107.
Examples
x <- iris[,1:4]
z <- harris.test(x, test = "robust")
z